Associated Papers
Neoantigenic properties of TP53 variants influence cancer risk in individuals with Li-Fraumeni syndrome.
Montellier E et al.
Ebiomedicine, 2026
41370968 (PubMed )
Supplement: mmc2.xlsx › Table_S1
A quantitative, Bayesian-informed approach to gene-specific variant classification: Updated Expert Panel recommendations improve classification of TP53 germline variants for Li-Fraumeni syndrome.
Fortuno C et al.
Genome Medicine, 2025
41126324 (PubMed )
Supplement: 13073_2025_1536_MOESM2_ESM.xlsx › Table_S2
Clustering of TP53 variants into functional classes correlates with cancer risk and identifies different phenotypes of Li-Fraumeni syndrome.
Montellier E et al.
Iscience, 2024
39634561 (PubMed )
Supplement: mmc2.xlsx › TableS1
Multiregion exome sequencing indicates a monoclonal origin of esophageal spindle-cell squamous cell carcinoma.
Wang Y et al.
The Journal of Pathology, 2024
TP53 germline pathogenic variants in modern humans were likely originated during recent human history.
Kou SH et al.
Nar Cancer, 2023
Different impacts of TP53 mutations on cell cycle-related gene expression among cancer types.
Sasaki K et al.
Scientific Reports, 2023
36964217 (PubMed )
Supplement: 41598_2023_32092_MOESM1_ESM.xlsx › Suppl._table_S1
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.
Ben-Cohen G et al.
Briefings in Bioinformatics, 2022
35043155 (PubMed )
Supplement: supplementary_table_s1_bbab524.xlsx › S1B._Negative_Set
Identification of relevant genetic alterations in cancer using topological data analysis.
Rabadán R et al.
Nature Communications, 2020
32732999 (PubMed )
Supplement: 41467_2020_17659_MOESM3_ESM.xls › TableS1
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Brown AL et al.
Plos Computational Biology, 2019
31034466 (PubMed )
Supplement: pcbi.1006981.s010.xlsx › final_benchmark
Germ-line and somatic EPHA2 coding variants in lens aging and cataract.
Bennett TM et al.
Plos One, 2017
29267365 (PubMed )
Supplement: pone.0189881.s007.xlsx › S7a._Clear__1__all
Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.
Engin HB et al.
Plos One, 2016
27043210 (PubMed )
Supplement: pone.0152929.s003.xlsx › mut_effects_per_ppi
Integrated mRNA and microRNA transcriptome sequencing characterizes sequence variants and mRNA-microRNA regulatory network in nasopharyngeal carcinoma model systems.
Szeto CY et al.
Febs Open Bio, 2014
24490137 (PubMed )
Supplement: mmc5.xlsx › coding
DNA methyltransferase and alcohol dehydrogenase: gene-nutrient interactions in relation to risk of colorectal polyps.
Jung AY et al.
Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 2008
DNMT3B polymorphisms and risk of primary lung cancer.
Lee SJ et al.
Carcinogenesis, 2005
Reassessment of the TP53 mutation database in human disease by data mining with a library of TP53 missense mutations.
Soussi T et al.
Human Mutation, 2005
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